Disentangled representations.
The aim of unsupervised disentangled representation learning is to discover underlying factors of variation in a training data, in which each dimension encodes a unique and semantically meaningful aspect of the data [1]}. To this end, most of the existing approaches are based on VAEs [2]}, [3]} with slight modifications in the VAE objective, such as \(\beta \) -VAE [4]}, FactorVAE [5]}. Similarly, some prior work tweak the objective of GANs [6]} to achieve disentanglement, e.gInfoGAN [7]}, IB-GAN [8]}. Locatello et al [9]} showed that unsupervised learning of disentangled generative factors can not be achieved without strong inductive biases on both the models and the data, which can be alleviated using weak supervision or a few labeled training samples [10]}. Key to the success of our model, in our work we especially focus on disentangling motion and content, which has been previously studied in a fully unsupervised setting [11]}, or using action/attribute labels [12]}. But we instead utilize natural language descriptions as weak supervision.
